{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Catapult Project - Australian Geoscience Datacube API\n",
    "Perform band maths and produce a Normalised Difference Vegetation Index (NDVI) file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import xarray\n",
    "import datacube.api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dc = datacube.api.API()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "alos2 = dc.get_dataset(product='gamma0', platform='ALOS_2', \n",
    "                      y=(-42.55,-42.57), x=(147.55,147.57), \n",
    "                      variables=['hh_gamma0', 'hv_gamma0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s1a = dc.get_dataset(product='gamma0', platform='SENTINEL_1A', \n",
    "                      y=(-42.55,-42.57), x=(147.55,147.57), \n",
    "                      variables=['vh_gamma0', 'vv_gamma0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hh = alos2.hh_gamma0\n",
    "hv = alos2.hv_gamma0\n",
    "vh = s1a.vv_gamma0\n",
    "vv = s1a.vv_gamma0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the first time index and plot the first timeslice (only one timeslice in this example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2.71500000e+03,   2.19400000e+03,   2.32700000e+03,\n",
       "          2.57200000e+03,   2.58400000e+03,   2.50300000e+03,\n",
       "          2.20000000e+03,   1.97800000e+03,   1.72200000e+03,\n",
       "          1.35500000e+03,   1.17600000e+03,   1.00900000e+03,\n",
       "          8.39000000e+02,   6.74000000e+02,   5.28000000e+02,\n",
       "          4.55000000e+02,   3.96000000e+02,   3.11000000e+02,\n",
       "          2.45000000e+02,   2.27000000e+02,   1.75000000e+02,\n",
       "          1.55000000e+02,   1.23000000e+02,   9.80000000e+01,\n",
       "          1.04000000e+02,   6.50000000e+01,   6.30000000e+01,\n",
       "          5.60000000e+01,   5.20000000e+01,   3.20000000e+01,\n",
       "          3.60000000e+01,   3.30000000e+01,   2.50000000e+01,\n",
       "          1.40000000e+01,   2.00000000e+01,   1.40000000e+01,\n",
       "          7.00000000e+00,   7.00000000e+00,   8.00000000e+00,\n",
       "          7.00000000e+00,   1.30000000e+01,   1.00000000e+01,\n",
       "          6.00000000e+00,   7.00000000e+00,   7.00000000e+00,\n",
       "          2.00000000e+00,   2.00000000e+00,   3.00000000e+00,\n",
       "          1.00000000e+00,   1.00000000e+00,   1.00000000e+00,\n",
       "          1.00000000e+00,   2.00000000e+00,   0.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          1.00000000e+00,   1.00000000e+00,   0.00000000e+00,\n",
       "          2.00000000e+00,   1.00000000e+00,   1.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          1.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "          0.00000000e+00]),\n",
       " array([ 0.        ,  0.00390625,  0.0078125 ,  0.01171875,  0.015625  ,\n",
       "         0.01953125,  0.0234375 ,  0.02734375,  0.03125   ,  0.03515625,\n",
       "         0.0390625 ,  0.04296875,  0.046875  ,  0.05078125,  0.0546875 ,\n",
       "         0.05859375,  0.0625    ,  0.06640625,  0.0703125 ,  0.07421875,\n",
       "         0.078125  ,  0.08203125,  0.0859375 ,  0.08984375,  0.09375   ,\n",
       "         0.09765625,  0.1015625 ,  0.10546875,  0.109375  ,  0.11328125,\n",
       "         0.1171875 ,  0.12109375,  0.125     ,  0.12890625,  0.1328125 ,\n",
       "         0.13671875,  0.140625  ,  0.14453125,  0.1484375 ,  0.15234375,\n",
       "         0.15625   ,  0.16015625,  0.1640625 ,  0.16796875,  0.171875  ,\n",
       "         0.17578125,  0.1796875 ,  0.18359375,  0.1875    ,  0.19140625,\n",
       "         0.1953125 ,  0.19921875,  0.203125  ,  0.20703125,  0.2109375 ,\n",
       "         0.21484375,  0.21875   ,  0.22265625,  0.2265625 ,  0.23046875,\n",
       "         0.234375  ,  0.23828125,  0.2421875 ,  0.24609375,  0.25      ,\n",
       "         0.25390625,  0.2578125 ,  0.26171875,  0.265625  ,  0.26953125,\n",
       "         0.2734375 ,  0.27734375,  0.28125   ,  0.28515625,  0.2890625 ,\n",
       "         0.29296875,  0.296875  ,  0.30078125,  0.3046875 ,  0.30859375,\n",
       "         0.3125    ,  0.31640625,  0.3203125 ,  0.32421875,  0.328125  ,\n",
       "         0.33203125,  0.3359375 ,  0.33984375,  0.34375   ,  0.34765625,\n",
       "         0.3515625 ,  0.35546875,  0.359375  ,  0.36328125,  0.3671875 ,\n",
       "         0.37109375,  0.375     ,  0.37890625,  0.3828125 ,  0.38671875,\n",
       "         0.390625  ,  0.39453125,  0.3984375 ,  0.40234375,  0.40625   ,\n",
       "         0.41015625,  0.4140625 ,  0.41796875,  0.421875  ,  0.42578125,\n",
       "         0.4296875 ,  0.43359375,  0.4375    ,  0.44140625,  0.4453125 ,\n",
       "         0.44921875,  0.453125  ,  0.45703125,  0.4609375 ,  0.46484375,\n",
       "         0.46875   ,  0.47265625,  0.4765625 ,  0.48046875,  0.484375  ,\n",
       "         0.48828125,  0.4921875 ,  0.49609375,  0.5       ,  0.50390625,\n",
       "         0.5078125 ,  0.51171875,  0.515625  ,  0.51953125,  0.5234375 ,\n",
       "         0.52734375,  0.53125   ,  0.53515625,  0.5390625 ,  0.54296875,\n",
       "         0.546875  ,  0.55078125,  0.5546875 ,  0.55859375,  0.5625    ,\n",
       "         0.56640625,  0.5703125 ,  0.57421875,  0.578125  ,  0.58203125,\n",
       "         0.5859375 ,  0.58984375,  0.59375   ,  0.59765625,  0.6015625 ,\n",
       "         0.60546875,  0.609375  ,  0.61328125,  0.6171875 ,  0.62109375,\n",
       "         0.625     ,  0.62890625,  0.6328125 ,  0.63671875,  0.640625  ,\n",
       "         0.64453125,  0.6484375 ,  0.65234375,  0.65625   ,  0.66015625,\n",
       "         0.6640625 ,  0.66796875,  0.671875  ,  0.67578125,  0.6796875 ,\n",
       "         0.68359375,  0.6875    ,  0.69140625,  0.6953125 ,  0.69921875,\n",
       "         0.703125  ,  0.70703125,  0.7109375 ,  0.71484375,  0.71875   ,\n",
       "         0.72265625,  0.7265625 ,  0.73046875,  0.734375  ,  0.73828125,\n",
       "         0.7421875 ,  0.74609375,  0.75      ,  0.75390625,  0.7578125 ,\n",
       "         0.76171875,  0.765625  ,  0.76953125,  0.7734375 ,  0.77734375,\n",
       "         0.78125   ,  0.78515625,  0.7890625 ,  0.79296875,  0.796875  ,\n",
       "         0.80078125,  0.8046875 ,  0.80859375,  0.8125    ,  0.81640625,\n",
       "         0.8203125 ,  0.82421875,  0.828125  ,  0.83203125,  0.8359375 ,\n",
       "         0.83984375,  0.84375   ,  0.84765625,  0.8515625 ,  0.85546875,\n",
       "         0.859375  ,  0.86328125,  0.8671875 ,  0.87109375,  0.875     ,\n",
       "         0.87890625,  0.8828125 ,  0.88671875,  0.890625  ,  0.89453125,\n",
       "         0.8984375 ,  0.90234375,  0.90625   ,  0.91015625,  0.9140625 ,\n",
       "         0.91796875,  0.921875  ,  0.92578125,  0.9296875 ,  0.93359375,\n",
       "         0.9375    ,  0.94140625,  0.9453125 ,  0.94921875,  0.953125  ,\n",
       "         0.95703125,  0.9609375 ,  0.96484375,  0.96875   ,  0.97265625,\n",
       "         0.9765625 ,  0.98046875,  0.984375  ,  0.98828125,  0.9921875 ,\n",
       "         0.99609375,  1.        ]),\n",
       " <a list of 256 Patch objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S75HIB/oskyTtRhZPtzDJM4B/CTwkydt7Fj0AWDTIhkmS5r5pQwTYE9ivrbd/T/ldwL8b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13621d34f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hv.isel(time=0).plot.hist(bins=256, range=(0.0,1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a band just for fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x13624c41a90>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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du4RX5YBXI7teI+/tWBULUj1rTYLDR9pVTz2iaztbuqLKDpZmQePYD1wYQjgJ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\nrIgDh5mZFfkN93RTPSg4TvwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136243f6470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vv.isel(time=0).plot.imshow(cmap=\"spectral\", clim=(0.0, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select a range on the spatial dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insert some rsgislib segmentation magic below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The xarray can be turned into a Dataset, which can then be saved across all of the timeseries to a NetCDF file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds_vv = vv[:100,:100,:100].to_dataset(name='vv')\n",
    "ds_vv.to_netcdf('vv.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transposing dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging two datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 1, x: 156, y: 187)\n",
      "Coordinates:\n",
      "  * time       (time) datetime64[ns] 2016-03-01T23:59:59\n",
      "  * y          (y) float64 -4.731e+06 -4.731e+06 -4.731e+06 -4.731e+06 ...\n",
      "  * x          (x) float64 1.311e+06 1.311e+06 1.311e+06 1.311e+06 1.311e+06 ...\n",
      "Data variables:\n",
      "    vh_gamma0  (time, y, x) float32 0.0164571 0.0410914 0.0317888 0.0190076 ...\n",
      "    vv_gamma0  (time, y, x) float32 0.0891735 0.0691831 0.128744 0.122255 ...\n",
      "    crs        int32 0\n",
      "    hh_gamma0  (time, y, x) float32 0.0655217 0.143631 0.11165 0.116711 ...\n",
      "    hv_gamma0  (time, y, x) float32 0.0378705 0.0161559 0.0106346 0.00677407 ...\n",
      "Attributes:\n",
      "    license: Creative Commons Attribution 4.0 International CC BY 4.0\n",
      "    source: This data is a reprojection and retile of Landsat surface reflectance data from the USGS\n",
      "    summary: These files are experimental, short lived, and the format will change.\n",
      "    title: Experimental Data files From the Australian Geoscience Data Cube - DO NOT USE\n",
      "    product_version: 0.0.0\n"
     ]
    }
   ],
   "source": [
    "alos2_array = dc.get_dataset(product='gamma0', platform='ALOS_2', \n",
    "                      y=(-42.55,-42.57), x=(147.55,147.57), \n",
    "                      variables=['hh_gamma0', 'hv_gamma0'])\n",
    "s1a_array = dc.get_dataset(product='gamma0', platform='SENTINEL_1A', \n",
    "                      y=(-42.55,-42.57), x=(147.55,147.57), \n",
    "                      variables=['vh_gamma0', 'vv_gamma0'])\n",
    "\n",
    "sar_array = s1a_array.merge(alos2_array)\n",
    "print (sar_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will clip and scale the image to improve the contrast of the visible bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fake_saturation = 0.05\n",
    "clipped_sar = sar_array.where(sar_array<fake_saturation).fillna(fake_saturation)\n",
    "max_val = clipped_sar.max(['y', 'x'])\n",
    "scaled = (clipped_sar / max_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frozen(SortedKeysDict({'y': 187, 'x': 156, 'time': 1}))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgb = scaled.transpose('time', 'y', 'x')\n",
    "rgb.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Image data can not convert to float",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-64-292cc5735cee>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrgb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\Simon\\Anaconda3\\envs\\py35\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, **kwargs)\u001b[0m\n\u001b[0;32m   3020\u001b[0m                         \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3021\u001b[0m                         \u001b[0mimlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3022\u001b[1;33m                         **kwargs)\n\u001b[0m\u001b[0;32m   3023\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3024\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Simon\\Anaconda3\\envs\\py35\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1809\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m   1810\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1811\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1812\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1813\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Simon\\Anaconda3\\envs\\py35\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   4945\u001b[0m                               resample=resample, **kwargs)\n\u001b[0;32m   4946\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4947\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4948\u001b[0m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4949\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Simon\\Anaconda3\\envs\\py35\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36mset_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    447\u001b[0m         if (self._A.dtype != np.uint8 and\n\u001b[0;32m    448\u001b[0m                 not np.can_cast(self._A.dtype, np.float)):\n\u001b[1;32m--> 449\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Image data can not convert to float\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    450\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    451\u001b[0m         if (self._A.ndim not in (2, 3) or\n",
      "\u001b[1;31mTypeError\u001b[0m: Image data can not convert to float"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQkAAAEACAYAAACgZ4OsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13625e274a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(rgb.isel(time=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.image\n",
    "matplotlib.image.imsave('vv_hh_vh.png', rgb.isel(time=16))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Behind the scenes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ndvi result is performed by chaining a series of operations together on a per-chunk basis. The execution tree, including the masking on the `nodata` value done by the API, ends up being the same for each chunk. The graph can be read from the bottom up, with the ouput array chunks at the top.\n",
    "(Double-click the tree-graph image below to zoom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sar.data.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look at a single chunk, the NDVI calculation can be seen where the lines cross over to the `add` and `sub` circles.\n",
    "Some optimizarion has taken place: the `div` operation has been combined with another inline function, and the other chunks have been discarded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "partial = vv[0,0,0]\n",
    "partial.data.visualize(optimize_graph=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
